Comparative analysis of three methods for estimating the compositions of construction waste

Abstract

Determination of the relative compositions of the mixed construction waste is crucial and an important step to enhance resource management. This information influences the design of construction waste recycling and sorting facilities, and aids in formulating effective management strategies for recycled and sorted waste products. However, different methods for waste sorting and composition recognition possess distinct characteristics and only apply to specific practical scenarios. In this study, three methods are compared: (i) manual sorting as a reference method, (ii) a manual image recognition method using infrared thermal imaging, and (iii) a deep learning-based image recognition method based on the SegFormer semantic segmentation model. The comparison focuses on accuracy, preferences, prerequisites, socio-environmental impacts, costs, and improvement potential. Results show that both manual and deep learning-based image recognition methods yield comparable accuracy to manual sorting for inert waste, with relative errors below 5.2%, but relatively higher recognition errors for non-inert waste. Overall, manual sorting remains the most cost-effective and fastest method, despite its high labor demand, spatial constraints, environmental impacts, and limited improvement potential. In comparison, manual image recognition requires approximately 9.2 times the processing time and 2.3 times the cost of manual sorting, while deep learning-based image recognition incurs about 9.9 times the time and 2.5 times the cost. Nevertheless, both image recognition methods offer potential environmental benefits and long-term efficiency gains.